Adaptive deep brain stimulation based on variance

Abstract

Systems, devices, and methods support sensing one or more electrical signals from one or more electrodes in contact with or proximate to an anatomical element, determining variance values associated with a plurality of metric values of the one or more electrical signals, generating a control signal based on a comparison of the variance values to a threshold variance value, and delivering treatment for the anatomical element in response to the control signal.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 530,001, filed July 31, 2023, entitled “VARIANCE-BASED ADAPTIVE DEEPBRAIN STIMULATION”, the entire contents of which are hereby incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to neural stimulation, and more specifically to variance-based adaptive deep brain stimulation. Background Technology

[0004] Some devices can support brain stimulation for the treatment of medical conditions. Improved technologies for delivering stimulation and monitoring its effects are expected. Summary of the Invention

[0005] Examples of aspects of this disclosure include: A system includes: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: sense one or more electrical signals (e.g., multiple bioelectrical signals) from one or more electrodes in contact with or near an anatomical element; determine variance values ​​associated with multiple measures of the one or more electrical signals; generate a control signal based on a comparison of the variance values ​​with a threshold variance value; and deliver treatment to the anatomical element in response to the control signal.

[0006] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value or multiple threshold variance values; and a comparison of the variance value with a threshold range or multiple threshold ranges, wherein the generation of the control signal is based on the state.

[0007] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold measure, wherein the generation of the control signal is based on the state.

[0008] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0009] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of the following: the type associated with the one or more signals; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0010] In any aspect of this article, the instructions which can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: provide electrical signal stimulation to the anatomical element; and deliver a drug associated with the treatment of the anatomical element to the subject.

[0011] Any aspect of this document, wherein the plurality of measures includes the power values ​​of the one or more electrical signals.

[0012] Any aspect of this article, in which the anatomical element includes neural tissue.

[0013] A system includes: a treatment delivery device; a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: sense one or more electrical signals from one or more electrodes in contact with or near the anatomical element; determine variance values ​​associated with a plurality of measures of the one or more electrical signals; generate a control signal based on a comparison of the variance values ​​with a threshold variance value; and deliver treatment to the anatomical element via the treatment delivery device in response to the control signal.

[0014] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value or multiple threshold variance values; and a comparison of the variance value with a threshold range or multiple threshold ranges, wherein the generation of the control signal is based on the state.

[0015] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold, wherein the generation of the control signal is based on the state.

[0016] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0017] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of the following: the type associated with the one or more electrical signals; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0018] In any aspect of this article, the instructions which can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: provide electrical signal stimulation to the anatomical element; deliver a drug associated with the treatment of the anatomical element to the subject; or both.

[0019] A method includes: sensing one or more electrical signals from one or more electrodes in contact with or near an anatomical element via one or more sensors; calculating a variance value associated with a plurality of measures of the one or more electrical signals; generating a control signal based on a comparison of the variance value with a threshold variance value; transmitting the control signal electronically; and causing treatment to be delivered to the anatomical element in response to the control signal.

[0020] Any aspect of this document further includes: setting a state associated with the delivery of treatment against the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value or multiple threshold variance values; and a comparison of the variance value with a threshold range or multiple threshold ranges, wherein the generation of the control signal is based on the state.

[0021] Any aspect of this document further includes: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold, wherein the generation of the control signal is based on the state.

[0022] Any aspect of this document further includes setting one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0023] Any aspect of this document further includes setting one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of the following: the type associated with the one or more electrical signals; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0024] Any aspect of this article further includes: providing electrical signal stimulation to the anatomical element; and delivering a drug associated with the treatment of the anatomical element to the subject.

[0025] Any one aspect in combination with any one or more other aspects.

[0026] Any one or more of the features disclosed in this article.

[0027] As is the case with any one or more of the features substantially disclosed herein.

[0028] Combination of any one or more features as substantially disclosed herein with any one or more other features as substantially disclosed herein.

[0029] Any of these aspects / features / implementations in combination with any one or more other aspects / features / implementations.

[0030] Use of any one or more aspects or features as disclosed herein.

[0031] It should be understood that any feature described herein may be claimed in combination with any other feature(s) as described herein, regardless of whether such features are derived from the same implementation described.

[0032] Details of one or more aspects of this disclosure are set forth in the accompanying drawings and the following description. Other features, objects, and advantages of the technology described in this disclosure will become clear from the specification, drawings, and claims.

[0033] The foregoing is a simplified overview of this disclosure to provide an understanding of some aspects of it. This overview is neither a broad nor an exhaustive summary of this disclosure and its various aspects, implementations, and configurations. It is not intended to identify key or essential elements of this disclosure, nor to define its scope, but rather to present selected concepts in a simplified form as an introduction to the more detailed description that follows. As will be understood, other aspects, implementations, and configurations of this disclosure may utilize one or more features set forth above or described in detail below, either individually or in combination.

[0034] Many additional features and advantages of this disclosure will become apparent to those skilled in the art upon consideration of the embodiments described below. Attached Figure Description

[0035] The accompanying drawings are incorporated into and form part of this specification to illustrate several examples of this disclosure. These drawings, together with the specification, explain the principles of this disclosure. The drawings simply illustrate preferred and alternative examples of how this disclosure can be carried out and used, and should not be construed as limiting this disclosure to the examples shown and described. Further features and advantages will become apparent from the following more detailed description of various aspects, implementations, and configurations of this disclosure, as illustrated by the accompanying drawings referenced below.

[0036] Figure 1 Examples of systems based on various aspects of this disclosure are shown.

[0037] Figure 2 Examples of systems based on various aspects of this disclosure are shown.

[0038] Figures 3 to 5 Example graphs are shown illustrating various aspects of this disclosure.

[0039] Figure 6 Examples of systems based on various aspects of this disclosure are shown.

[0040] Figure 7 Examples of process flows for various aspects of this disclosure are shown. Detailed Implementation

[0041] It should be understood that the various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the example or implementation, certain actions or events of any process or method described herein may be performed in a different order, and / or may be added, combined, or completely omitted (e.g., implementing the technology disclosed according to different embodiments of this disclosure may not necessarily require all the described actions or events). Furthermore, although for clarity some aspects of this disclosure are described as being performed by a single module or unit, it should be understood that the technology of this disclosure can be performed by a combination of units or modules associated with, for example, computing devices and / or medical devices.

[0042] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, these functions may be stored as one or more instructions or code on a computer-readable storage medium and executed by a hardware-based processing unit. Alternatively or additionally, the functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). The computer-readable storage medium may include a non-transitory computer-readable medium, which corresponds to a tangible medium such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0043] The instructions can be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionic processors; or any other general-purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000 series processors, Nvidia GeForce RTX 3000 series processors, AMD Radeon RX 5000 series processors, AMD Radeon RX 6000 series processors, or any other graphics processing units), application-specific integrated circuits (ASICs), field-programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Accordingly, the term "processor" as used herein may refer to any structure of the above-described structure or any other physical structure suitable for implementing the described techniques. Furthermore, these techniques may be fully implemented in one or more circuit or logic elements.

[0044] Before explaining any implementation of this disclosure in detail, it should be understood that the application of this disclosure is not limited to the construction details and component arrangements set forth in the following description or shown in the accompanying drawings. This disclosure can have other implementations and can be practiced or implemented in various ways. Furthermore, it should be understood that the wording and terminology used herein are for descriptive purposes and should not be considered limiting. The use of “including,” “comprising,” or “having,” and variations thereof herein is intended to cover items listed below and their equivalents, as well as additional items. Further, this disclosure may use examples to illustrate one or more aspects thereof. Unless otherwise expressly stated, the use or enumeration of one or more examples (which may be expressed as “for example,” “by way of example,” “e.g.,” “for example,” or similar language) is not intended to, and will not, limit the scope of this disclosure.

[0045] The terms “proximal” and “distal” are used in this disclosure in their usual medical sense. “Proximal” is closer to the operator or user of the system and farther from the surgical area of ​​concern inside or on the patient, while “distal” is closer to the surgical area of ​​concern inside or on the patient and farther from the operator or user of the system.

[0046] Some adaptive deep brain stimulation (DBS) algorithms are based on thresholds of the input signal. However, in some cases, thresholding-based control techniques may be susceptible to signal drift or changes in the underlying signal distribution (e.g., due to temperature variations, electronic noise, power supply fluctuations, etc.). Other techniques may include adaptive neuromodulation associated with providing stimulation to the subject.

[0047] According to example aspects of this disclosure, systems and techniques supporting variance-based stimulation associated with the delivery of treatment to subjects are described. For example, these systems and techniques can support variance-based adaptive brain stimulation (e.g., variance-based adaptive DBS techniques). In some aspects of this disclosure, by using the variance of input signals (e.g., electrical signals, such as bioelectrical signals, environmental electrical signals, noise signals, combinations thereof, etc.), these systems and techniques can mitigate drift or variations in overall variability of the input signals. In some examples, as a result of mitigating drift or variations in overall variability, these systems and techniques can produce adaptive techniques for delivering stimulation that exhibit increased stability, responsiveness, and optimization relative to the patient's medical condition compared to other stimulation techniques.

[0048] Various aspects of this disclosure support threshold-based adaptive deep brain stimulation (DBS) systems based on input signals (e.g., bioelectrical signals) whose variance (e.g., standard deviation) can vary. For example, various aspects of this disclosure may include monitoring signal metrics (e.g., power, power variance, power standard deviation, etc.) of the input signals relative to time (e.g., in the time domain (TD)).

[0049] These systems and techniques can support the use of one or more metrics associated with the input signal as control signals for controlling treatment delivery. In some example implementations, these systems and techniques can support the use of moving variance as a primary (or secondary) input control signal for adapting treatment delivery. For example, these systems and techniques may include using moving variance as a control input for a DBS technique for delivering treatment, thereby providing a variance-based adaptive DBS (vaDBS) technique for treatment delivery. In some aspects, the example techniques described herein can resist signal drift associated with variations in signal amplitude and background noise.

[0050] In some examples, these systems and techniques can support setting a state associated with treatment delivery based on an input signal (e.g., a bioelectrical signal). For example, as will be described herein, these systems and techniques can include variance-based adaptive stimulation (e.g., vaDBS) that involves setting a state for treatment delivery (also referred to herein as a vaDBS state) based on a comparison of the variance of the input signal with a threshold variance value. In another example, as will be described herein, these systems and techniques can include setting a state for treatment delivery based on a comparison of variance with a threshold variance range or a comparison of another metric associated with the input signal (e.g., power, power range, etc.) with a corresponding threshold.

[0051] These systems and techniques can support multimodal approaches for stimulation. For example, they can support the delivery of therapy and stimulation using vaDBS-based stimulation modalities (e.g., stimulation based on the variance of bioelectrical signals). In some other aspects, these systems and techniques can support the delivery of therapy and stimulation using stimulation modalities based on the vaDBS techniques described herein and aDBS-based stimulation modalities (e.g., stimulation based on the power of measured bioelectrical signals, etc.). In some aspects, these systems and techniques can include delivering stimulation based on weighting factors applied respectively to each stimulation modality.

[0052] The systems and techniques described herein for deep brain stimulation are not limited thereto. For example, these systems and techniques can support the provision of clinical insights (e.g., biological responses) associated with the stimulation provided to a patient. In some aspects, these systems and techniques can support the automated programming of stimulation settings. In other aspects, these systems and techniques can support stimulation associated with other medical procedures (e.g., chronic deep brain stimulation (CDBS) involving the use of implanted electrodes to stimulate specific regions of the brain).

[0053] The embodiments of this disclosure provide technical solutions to one or more of the following problems: (1) signal drift and relatively large variations in background noise; and (2) measurement errors associated with bioelectrical signals that may affect the determination of parameters used to deliver treatment to patients.

[0054] Figure 1 Examples of system 100 that support the aspects of this disclosure are shown.

[0055] System 100 includes a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, a database 130, and / or a cloud network 134 (or other network). Other embodiments of the system according to this disclosure may include more or fewer components than system 100. For example, system 100 may omit and / or include additional instances of one or more components of the computing device 102, imaging devices(s) 112, robot 114, navigation system 118, database 130, and / or cloud network 134. In this example, system 100 may omit any instance of the computing device 102, imaging devices(s) 112, robot 114, navigation system 118, database 130, and / or cloud network 134. For example, system 100 may omit robot 114 and navigation system 118. System 100 may support implementations of one or more other aspects of the methods disclosed herein.

[0056] The computing device 102 includes a processor 104, a memory 106, a communication interface 108, and a user interface 110. Other embodiments of the computing device according to this disclosure may include more or fewer components than the computing device 102. The computing device 102 may be, for example, a control device including electronic circuitry associated with providing control signals to the stimulation device 154.

[0057] The processor 104 of computing device 102 may be any processor described herein or any similar processor. Processor 104 may be configured to execute instructions stored in memory 106, which may enable processor 104 to perform one or more computational steps using or based on data received from imaging device 112, robot 114, navigation system 118, electrode 150, stimulation device 154, database 130 and / or cloud 134.

[0058] Memory 106 may be or include RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible non-transitory memory used to store computer-readable data and / or instructions. Memory 106 may store information or data associated with performing any step of, for example, the methods or process flows described herein or any other method. Memory 106 may store instructions and / or machine learning models, for example, supporting one or more functions of imaging device 112, robot 114, navigation system 118, and stimulation device 154. For example, memory 106 may store content (e.g., instructions and / or machine learning models) that implements image processing 120, segmentation 122, transformation 124, registration 128, and / or navigation processing 129 when executed by processor 104. In some embodiments, such content, if provided in the form of instructions, may be organized into one or more applications, modules, packages, layers, or engines.

[0059] Alternatively or additionally, memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by processor 104 to implement the various methods and features described herein. Therefore, although the various contents of memory 106 can be described as instructions, it should be understood that the functions described herein can be implemented using instructions, techniques, and / or machine learning models. Data, techniques, and / or instructions can enable processor 104 to manipulate data stored in memory 106 and / or received from or via imaging device 112, robot 114, navigation system 118, database 130, cloud network 134, electrodes 150, and / or stimulation device 154.

[0060] The computing device 102 may also include a communication interface 108. The communication interface 108 may be used to receive data or other information from external sources (e.g., imaging device 112, robot 114, navigation system 118, database 130, cloud network 134, electrode 150, stimulation device 154 and / or any other system or component separate from system 100), and / or to transmit instructions, data (e.g., bioelectric signal 126, measurement data associated with bioelectric signal 126, control signals, etc.) or other information to external systems or devices (e.g., another computing device 102, imaging device 112, robot 114, navigation system 118, database 130, cloud network 134, electrode 150, stimulation device 154 and / or any other system or component not part of system 100). Communication interface 108 may include one or more wired interfaces (e.g., USB port, Ethernet port, FireWire port) and / or one or more wireless transceivers or interfaces (e.g., configured to send and / or receive information via one or more wireless communication protocols (e.g., 802.11a / b / g / n, Bluetooth, NFC, ZigBee, etc.)). In some embodiments, communication interface 108 may support communication between device 102 and one or more other processors 104 or computing devices 102, whether for reducing the time required to complete computationally intensive tasks or for any other reason.

[0061] The computing device 102 may also include one or more user interfaces 110. User interfaces 110 may be or include a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and / or any other device for receiving information from and / or providing information to a user. User interfaces 110 may be used, for example, to receive user selections or other user input regarding any step of any method described herein. Although as foregoing, any required input for any step of any method described herein may be automatically generated by system 100 (e.g., by processor 104 or another component of system 100) or received by system 100 from a source external to system 100. In some embodiments, user interfaces 110 may support user modification (e.g., by surgeons, medical personnel, patients, etc.) of instructions to be executed by processor 104 according to one or more embodiments of this disclosure, and / or user modification or adjustment of settings displayed on or corresponding to user interfaces 110.

[0062] In some embodiments, computing device 102 may utilize a user interface 110 housed separately from one or more other components of computing device 102. In some embodiments, user interface 110 may be located close to one or more other components of computing device 102, while in other embodiments, user interface 110 may be located away from one or more other components of computing device 102.

[0063] Imaging device 112 may be operable to image (e.g., bones, veins, tissues, etc.) and / or other aspects of a patient's anatomy to produce image data (e.g., image data depicting or corresponding to bones, veins, tissues, etc.). As used herein, "image data" means data generated or captured by imaging device 112, including data in machine-readable, graphical / visual, and any other form. In various examples, image data may include data corresponding to a patient's anatomical features or a portion thereof. Image data may be or include preoperative images, intraoperative images, postoperative images, or images taken independently of any surgical procedure. In some embodiments, first imaging device 112 may be used to acquire first image data (e.g., a first image) at a first time, and second imaging device 112 may be used to acquire second image data (e.g., a second image) at a second time after the first time. Imaging device 112 may be capable of capturing 2D or 3D images to produce image data. Imaging device 112 may be or include, for example: an ultrasound scanner (which may include, for example, physically separate transducers and receivers, or a single ultrasound transceiver), an O-arm, C-arm, G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluorescence microscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermal imaging camera (e.g., an infrared camera), a radar system (which may include, for example, a transmitter, a receiver, a processor, and one or more antennas), or any other imaging device 112 adapted to obtain images of a patient's anatomical features. Imaging device 112 may be entirely contained within a single housing, or may include transmitters / transmitters and receivers / detectors located in separate housings or otherwise physically separated.

[0064] In some embodiments, imaging device 112 may include more than one imaging device 112. For example, a first imaging device may provide first image data and / or a first image, and a second imaging device may provide second image data and / or a second image. In yet other embodiments, the same imaging device may be used to provide both the first image data and the second image data and / or any other image data described herein. Imaging device 112 may be operable to generate an image data stream. For example, imaging device 112 may be configured to operate with an open aperture or with an aperture that alternates continuously between open and closed in order to capture continuous images. For the purposes of this disclosure, unless otherwise stated, if the image data represents two or more frames per second, the image data may be considered continuous and / or provided as an image data stream.

[0065] Robot 114 can be any surgical robot or surgical robot system. Robot 114 can be, or includes, for example, Mazor X. TM Stealth robot guidance system. Robot 114 can be configured to position imaging device 112 in one or more precise locations and orientations, and / or return imaging device 112 to the same location(s) and orientation(s) at a later time point. Robot 114 can be additionally or alternatively configured to manipulate surgical instruments (whether or not based on guidance from navigation system 118) to perform or assist surgical tasks. In some embodiments, robot 114 can be configured to hold and / or manipulate anatomical elements during or in conjunction with surgical procedures. Robot 114 may include one or more robotic arms 116. In some embodiments, robotic arms 116 may include a first robotic arm and a second robotic arm, but robot 114 may include more than two robotic arms. In some embodiments, one or more of robotic arms 116 may be used to hold and / or manipulate imaging device 112. In embodiments where imaging device 112 includes two or more physically separate components (e.g., transmitter and receiver), one robotic arm 116 may hold one such component, and another robotic arm 116 may hold another such component. Each robotic arm 116 can be positioned independently of the other robotic arm. Robotic arms 116 can be controlled in a single shared coordinate space or in a separate coordinate space.

[0066] The robot 114, together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven, or more degrees of freedom. Furthermore, the robotic arm 116 can be positioned or localized in any pose, plane, and / or focal position. Pose includes position and orientation. Therefore, the imaging device 112, surgical instrument, or other object held by the robot 114 (or more specifically, held by the robotic arm 116) can be precisely positioned in one or more desired and specific locations and orientations.

[0067] The (multiple) robotic arms 116 may include one or more sensors that enable the processor 104 (or the processor of the robot 114) to determine the precise orientation of the robotic arms (and any objects or elements held or attached to the robotic arms) in space.

[0068] In some embodiments, reference markers (e.g., navigation markers) may be placed on robot 114 (including, for example, on robotic arm 116), imaging device 112, or any other object in the surgical space. The reference markers may be tracked by navigation system 118, and the results of the tracking may be used by the operator of robot 114 and / or by the operator of system 100 or any component thereof. In some embodiments, navigation system 118 may be used to track other components of the system (e.g., imaging device 112), and the system may operate without using robot 114 (e.g., a surgeon manually manipulating imaging device 112 and / or one or more surgical instruments, for example, based on information and / or instructions generated by navigation system 118).

[0069] During operation, navigation system 118 can provide navigation for the surgeon and / or surgical robot. Navigation system 118 can be any navigation system currently known or developed in the future, including, for example, Medtronic StealthStation. TM The S8 surgical navigation system or any subsequent system thereof. Navigation system 118 may include one or more cameras or (multiple) other sensors for tracking one or more reference markers, navigation trackers, or other objects within the operating room or other rooms where part or all of system 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some embodiments, navigation system 118 may include one or more tracking devices 140 (e.g., electromagnetic sensors, acoustic sensors, etc.).

[0070] In some aspects, navigation system 118 may include one or more of optical tracking systems, acoustic tracking systems, electromagnetic tracking systems, radar tracking systems, inertial measurement unit (IMU)-based tracking systems, and computer vision-based tracking systems. Navigation system 118 may include a corresponding transmission device 136 capable of transmitting signals associated with the tracking type. In some aspects, navigation system 118 may be capable of tracking objects present in images captured by imaging devices(s) 112 based on computer vision.

[0071] In various embodiments, navigation system 118 may be used to track the position and orientation (i.e., attitude) of imaging device 112, robot 114 and / or robotic arm 116, and / or one or more surgical tools (or more specifically, to track the attitude of navigation trackers directly or indirectly attached in a fixed relationship to one or more of the foregoing). Navigation system 118 may include a display for displaying one or more images from an external source (e.g., computing device 102, imaging device 112, or other sources), or for displaying images and / or video streams from one or more cameras or other sensors of navigation system 118.

[0072] In some implementations, system 100 may operate without the use of navigation system 118. Navigation system 118 may be configured to provide guidance to the surgeon or other users of system 100 or its components, to robot 114 or any other element of system 100 regarding, for example, the posture of one or more anatomical elements, whether the tool is in the appropriate trajectory, and / or how to move the tool into the appropriate trajectory to perform surgical tasks according to preoperative or other surgical plans.

[0073] Processor 104 can utilize data stored in memory 106 as a neural network. The neural network can include a machine learning architecture. In some aspects, the neural network can be or includes one or more classifiers. In other aspects, the neural network can be or includes any machine learning network, such as a deep learning network, convolutional neural network, reconstruction neural network, generative adversarial neural network, or any other neural network capable of implementing the functionality of the computing device 102 described herein. Some elements stored in memory 106 can be described or referred to as instructions or instruction sets, and some functions of computing device 102 can be implemented using machine learning techniques.

[0074] For example, processor 104 may support multiple machine learning models 138, which may be trained and / or updated based on data (e.g., training data 146) provided or accessed by any of computing device 102, imaging device 112, robot 114, navigation system 118, electrode 150, stimulation device 154, database 130, and / or cloud network 134. The multiple machine learning models 138 may be built and updated by computing device 102 based on training data 146 (also referred to herein as training data and feedback).

[0075] Example aspects of the electrode 150 and the stimulation device 154 will be described with reference to the following figures.

[0076] Database 130 may store information relating one coordinate system to another (e.g., one or more robot coordinate systems to a patient coordinate system and / or a navigation coordinate system). Database 130 may additionally or alternatively store, for example, one or more surgical plans (including, for example, patient treatment plans associated with electrode 150 and stimulation device 154); one or more images that may be used in connection with surgery to be performed by or with the assistance of one or more other components of system 100; and / or any other useful information. Database 130 may additionally or alternatively store, for example, the position or coordinates of stimulation device 154.

[0077] Database 130 can be configured to provide any such information directly or via cloud network 134 to computing device 102, or to any other device within or outside system 100. In some embodiments, database 130 may include patient-associated treatment information (e.g., treatment delivery plans). In some embodiments, database 130 may be or include part of a hospital image storage system, such as a Picture Archiving and Communication System (PACS), a Health Information System (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data.

[0078] In some aspects, computing device 102 can communicate directly or indirectly with (multiple) servers and / or databases (e.g., database 130) via a communication network (e.g., cloud network 134). The communication network may include any type of known communication medium or collection of communication media, and data can be transferred between endpoints using any type of protocol. The communication network may include wired communication technologies, wireless communication technologies, or any combination thereof.

[0079] Wired communication technologies may include, for example, Ethernet-based wired local area network (LAN) connections using physical transmission media (e.g., coaxial cable, copper cable / wire, fiber optic cable, etc.). Wireless communication technologies may include, for example, cellular or cellular data connections and protocols (e.g., digital cellular, Personal Communication Services (PCS), Cellular Digital Packet Data (CDPD), General Packet Radio Service (GPRS), Enhanced Data Rate Global System for Mobile Communications (GSM) Evolution (EDGE), Code Division Multiple Access (CDMA), Single Carrier Radio Transmission Technology (1×RTT), Evolved Data Optimized (EVDO), High-Speed ​​Packet Access (HSPA), Universal Mobile Telecommunications Service (UMTS), 3G, Long Term Evolution (LTE), 4G, and / or 5G, etc.), Bluetooth®, Bluetooth Low Energy®, Wi-Fi, radio, satellite, infrared connections, and / or ZigBee® communication protocols.

[0080] The Internet is an example of a communication network constituting an Internet Protocol (IP) network, which comprises multiple computers, computing networks, and other communication devices located in multiple locations, and whose components (e.g., computers, computing networks, communication devices) can be connected via one or more telephone systems and other devices. Other examples of communication networks may include, but are not limited to, Standard Simple Old-Style Telephone Systems (POTS), Integrated Services Digital Network (ISDN), Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Wireless LAN (WLAN), Session Initiation Protocol (SIP) networks, Voice over Internet Protocol (VoIP) networks, cellular networks, and any other type of packet-switched or circuit-switched network known in the art. In some cases, a communication network may include any combination of networks or network types. In some aspects, a communication network may include any combination of communication media used for transmitting data (e.g., transmitting / receiving data), such as coaxial cable, copper cable / wire, fiber optic cable, or antenna.

[0081] The computing device 102 can be connected to the cloud network 134 via a wired connection, a wireless connection, or both via a communication interface 108. In some embodiments, the computing device 102 can communicate with the database 130 and / or external devices (e.g., computing devices) via the cloud network 134.

[0082] System 100 or similar systems may be used, for example, to implement one or more aspects of any of the methods or processes described herein. System 100 or similar systems may also be used for other purposes.

[0083] Figure 2 Example implementations of a system 100 supporting variance-based stimuli according to various aspects of this disclosure are shown. For brevity, previous references have been omitted. Figure 1 The description includes aspects of system 100 and descriptions of similar components.

[0084] In the example, electrode 150 may contact or be close to anatomical element 149 of subject 148. Electrode 150 may support sensing bioelectrical signals 126 of subject 148. Figure 2 In the example, anatomical element 149 may be neural tissue. For example, anatomical element 149 may include neural tissue of the brain of subject 148, and sensing may include receiving one or more signals from the neural tissue (e.g., EEG, ECoG, MEG, LFP, etc.). This disclosure is not limited to this example aspect, and anatomical element 149 may include other neural tissue of subject 148.

[0085] The stimulation device 154 can provide the computing device 102 with data 125 associated with the subject 148 (e.g., bioelectrical signal 126). In some aspects, the computing device 102 can calculate measurement data associated with the bioelectrical signal 126 (e.g., metric values, power values, variance, etc.). In some other aspects, the stimulation device 154 can calculate measurement data or a portion thereof and provide that measurement data to the computing device 102. Based on the metric values ​​of the bioelectrical signal 126, the computing device 102 can generate a control signal 155 and provide it to the stimulation device 154 for delivering treatment to the subject 148. For example, based on the variance 170 (also referred to herein as variance value) between two or more metric values ​​of the bioelectrical signal 126, the computing device 102 can generate the control signal 155 and provide it to the stimulation device 154. Non-limiting examples of metric values ​​include signal amplitude, signal envelope, peak-to-peak amplitude, latency, decay, normalized variance, frequency of the variance signal, spectral information, etc. It should also be understood that the metric can be used to determine different types of variance values, including variance values ​​in the time domain, variance values ​​in the spectral domain, or combinations thereof.

[0086] refer to Figure 2 Example implementations are described in example graphs 160-a to 162-a. Graph 160-a includes a vertical axis corresponding to the power 165 of the bioelectrical signal 126 (in squared voltage) and an horizontal axis corresponding to time (in milliseconds). Graph 161-a includes a vertical axis corresponding to the variance 170-a (in squared voltage) and an horizontal axis corresponding to time (in milliseconds). Graph 162-a includes a vertical axis corresponding to the state value (e.g., between 0.0 and 1.0) and an horizontal axis corresponding to time (in milliseconds).

[0087] The stimulation device 154 can sense a bioelectrical signal 126 from an electrode 150 (or multiple electrodes 150) that is in contact with or near an anatomical element 149 of the subject 148. The computing device 102 (or the stimulation device 154) can calculate a metric of the bioelectrical signal 126. In this example, the metric of the bioelectrical signal 126 may include a power value.

[0088] In some respects, computing device 102 (or stimulation device 154) can determine additional measures associated with bioelectrical signal 126. For example, referring to graph 161-a, computing device 102 can calculate the variance 170-a associated with the measure of bioelectrical signal 126. An example of variance 170-a versus time (e.g., seconds) is shown in graph 161-a.

[0089] The computing device 102 can generate a control signal 155 based on a metric associated with the bioelectrical signal 126. For example, in conjunction with a vaDBS mode, the computing device 102 can generate the control signal 155 based on a comparison of variance 170-a with a threshold variance value 171-a. In one example, in response to a comparison result (e.g., where variance 170-a is greater than the threshold variance value 171-a), the computing device 102 can generate the control signal 155 and provide it to the stimulation device 154. In another example, in response to a different comparison result (e.g., where variance 170-a is less than the threshold variance value 171-a), the computing device 102 can avoid providing the control signal 155 to the stimulation device 154. In some other example implementations, computing device 102 may generate and provide control signals in response to a comparison result where variance 170-a is greater than a threshold variance value 171-a, and computing device 102 may generate and provide different control signals in response to a comparison result where variance 170-a is less than the threshold variance value 171-a. It should also be understood that computing device 102 may generate and provide control signals in response to a comparison of variance 170-a with multiple threshold variance values ​​(e.g., an upper threshold and a lower threshold).

[0090] In another example, in conjunction with the aDBS mode, computing device 102 can generate control signal 155 based on a comparison of a metric (e.g., power) with a threshold 166-a (e.g., power). In this example, in response to a comparison result (e.g., where the metric is greater than threshold 166-a), computing device 102 can generate control signal 155 and provide it to stimulation device 154. In another example, in response to a different comparison result (e.g., where the metric is less than threshold 166-a), computing device 102 can avoid providing control signal 155 to stimulation device 154. In some other example implementations, computing device 102 can generate and provide control signal in response to a comparison result where the metric is greater than threshold 166-a, and computing device 102 can generate and provide different control signals in response to a comparison result where the metric is less than threshold 166-a.

[0091] The measurement that computing device 102 can use to generate and provide control signal 155 is an example, and aspects of this disclosure are not limited thereto. Other non-limiting examples of parameters that computing device 102 can use to evaluate variance 170-a may include a threshold variance range 172, a baseline value (not shown) associated with variance 170-a, etc. For example, computing device 102 can generate and provide control signal 155 and can set one or more parameters associated with treatment delivery based on the deviation between variance 170-a and range 172 (e.g., variance 170-a being greater than the upper limit of range 172 or less than the lower limit of range 172), the deviation between variance 170 and baseline value, etc. Other non-limiting examples of parameters that computing device 102 can use to evaluate variance 170-a may include a dynamic threshold or target value associated with variance 170-a. For example, computing device 102 can compare variance 170-a to different thresholds corresponding to different states of subject 148 (e.g., sleep, wakefulness, exercise, etc.).

[0092] The stimulation device 154 can deliver treatment to the subject 148 in response to a control signal 155. In some aspects, the stimulation device 154 can deliver treatment to the subject 148 for the anatomical element 149 and / or another anatomical element 149. In an example of delivering treatment, the stimulation device 154 can provide electrical stimulation to the anatomical element 149 via electrodes 150. In another example (not shown), the stimulation device 154 can deliver a drug to the subject 148 associated with the anatomical element 149 and / or another anatomical element 149 treating the subject 148.

[0093] System 100 can support multimodal methods for delivering treatment (e.g., stimulation, medication, etc.) to subject 148. For example, system 100 can support setting different states for delivering treatment to subject 148. These states can be associated with different stimulation modes (e.g., aDBS, vaDBS) as described herein. In an example, system 100 can support setting states based on a metric of bioelectrical signal 126 and properties associated with the metric (e.g., variance 170, deviation of variance 170, etc.). Examples of the different states described herein (e.g., state 175 and state 180) are shown at plot 162-a.

[0094] In the example, computing device 102 may set a state 175-a (e.g., vaDBS state) associated with treatment delivery based on a comparison of variance 170-a with a threshold variance value 171-a. Computing device 102 may generate and provide control signal 155 based on state 175-a. In the example, computing device 102 generates and provides control signal 155 in response to a low state (e.g., state 175-a = '0'), and computing device 102 is exempt from generating / providing control signal 155 in response to a high state (e.g., state 175-a = '1').

[0095] In another example, computing device 102 may set a state 180 (e.g., aDBS state) associated with treatment delivery based on a comparison of a metric (described with reference to graph 160-a) with a threshold 166-a. Computing device 102 may generate and provide control signal 155 based on state 180-a. In this example, computing device 102 generates and provides control signal 155 in response to a low state (e.g., state 180-a = '0'), and computing device 102 is exempt from generating / providing control signal 155 in response to a high state (e.g., state 180-a = '1').

[0096] In another example (not shown), computing device 102 may set a state associated with the delivery of treatment to anatomical element 149 based on a comparison of variance 170-a with a threshold range value 172-a or multiple threshold ranges. Computing device 102 may then generate a control signal 155 based on this state.

[0097] Various aspects of this disclosure can support multiple thresholds. For example, referring to graph 160-a, system 100 can support additional thresholds associated with generating and providing control signal 155 (or a different control signal 155) and setting state 180-a. In another example, referring to graph 161-a, system 100 can support additional threshold variance values ​​(not shown) associated with generating and providing control signal 155 (or a different control signal 155) and setting state 175-a. In some other examples, system 100 can support thresholds (not shown) associated with any measure of bioelectrical signal 126.

[0098] In some aspects, system 100 can support different therapy profiles associated with providing treatment to subject 148. System 100 can apply any of these therapy profiles based on one or more criteria. In some aspects, system 100 can apply therapy profiles based on the type of metric associated with assessing bioelectrical signal 126 (e.g., power 165, variance 170, etc.). In other aspects, system 100 can apply different therapy profiles based on the metric values ​​of the corresponding metric type.

[0099] For example, computing device 102 can apply a treatment profile associated with the value of variance 170-a. For example, computing device 102 can apply different treatment profiles depending on whether variance 170-a is included within or outside the range 172-a. In another example, computing device 102 can apply a second treatment profile associated with a metric of bioelectrical signal 126 (e.g., power 165).

[0100] The computing device 102 can set one or more parameters associated with the delivery of treatment based on a treatment profile. In an example where the delivery of treatment includes providing electrical stimulation, the one or more parameters may include the frequency associated with the delivery of the electrical pulse, the amplitude (intensity) of the current, the pulse width (duration) of the electrical pulse, the stimulation mode (e.g., continuous stimulation, intermittent stimulation, burst stimulation, etc.), the polarity of the current, etc. In an example where the delivery of treatment includes delivering a drug to the subject 148, the one or more parameters may include the frequency, dosage, etc. associated with the administration of the drug.

[0101] Therefore, for example, system 100 can support guided programming for delivering treatment in association with various condition types, such as the power level of bioelectrical signal 126 being higher or lower than threshold 166-a, variance 170-a being higher or lower than threshold variance value 171-a, etc. In some aspects, each treatment profile can correspond to various device settings of stimulation device 154. As described herein, system 100 can support multiple control techniques based on the same dataset.

[0102] System 100 may support features for setting sensing parameters associated with the sensed bioelectric signal 126 based on one or more criteria. For example, system 100 may support setting parameters based on the type associated with the bioelectric signal 126, the variance 170 of the bioelectric signal 126, the metric of the bioelectric signal 126 (e.g., threshold 166), whether the variance 170 is within the threshold variance range 172, etc.

[0103] Example sensing parameters include electrode configuration, sampling rate, and sensing center frequency. In the example, system 100 may implement the electrode configuration as a primary sensing parameter and / or an independent sensing parameter (e.g., among multiple sensing parameters). In another example, system 100 may set the sampling rate to a default value, and the sampling rate may be fixed or configurable. In some other examples, system 100 may utilize different center frequencies corresponding to different symptoms.

[0104] System 100 can support the application of weighting factors to any parameter described herein associated with generating control signal 155 and delivering treatment. For example, system 100 can support the application of corresponding weighting factors to comparisons associated with a metric (e.g., power 165) and a threshold 166, comparisons associated with variance 170-a and threshold variance 171-a, comparisons associated with variance 170-a and range 172-a, etc. In some aspects, system 100 can support the application of corresponding weighting factors to states associated with comparisons (e.g., state 175-a, state 180-a, etc.). Thus, for example, system 100 can support treatment delivery based on the weighting factor applied to each parameter (e.g., comparison of metric with threshold 166-a, comparison of variance 170-a with threshold variance 171-a, comparison of variance 170-a with range 172-a, etc.).

[0105] In some aspects, such as Figure 2 As shown, state 175-a (e.g., vaDBS state) can be the opposite of state 180-a (e.g., aDBS state). In some respects, referring to the example of curve 162-a, the duration for which state 175-a is '0' and state 180-a is '1' (e.g., after approximately 570 ms) can be referred to as a steady state. In some cases, system 100 can support tracking multiple variables. In the example, system 100 can generate control signals and / or guide therapy to increase stimulation when the activity of a first tracked variable (e.g., one of state 175-a or state 180-a) is relatively high, and alternatively or additionally, generate control signals and / or guide therapy to decrease stimulation when the activity of a second tracked variable (e.g., the other of state 175-a or state 180-a) is relatively high.

[0106] Figures 3 to 5Additional examples of power 165, threshold 166, variance 170, threshold variance value 171, state 175, and state 180 are shown in graphs 160 (e.g., graphs 160-b to 160-d), 161 (e.g., graphs 161-b to 161-d), and 162 (e.g., graphs 162-b to 162-d) according to various aspects of this disclosure.

[0107] Graphs 160 (e.g., graphs 160-b to 160-d), 161 (e.g., graphs 161-b to 161-d), and 162 (e.g., graphs 162-b to 162-d) may include aspects of graphs 160-a to 162-a. For example, graphs 160-b to 160-d each include a vertical axis corresponding to the power 165 of the bioelectrical signal 126 (in units of voltage squares) and an horizontal axis corresponding to time (in milliseconds). Graphs 161-b to 161-d each include a vertical axis corresponding to the variance 170 (in units of voltage squares) and an horizontal axis corresponding to time (in milliseconds). Curves 162-b to 162-d each include a vertical axis corresponding to the state value (e.g., between 0.0 and 1.0) and a horizontal axis corresponding to time (in milliseconds).

[0108] refer to Figure 3 For example, graph 160-b illustrates an example of signal drift associated with the power 165-b of the bioelectric signal 126. Various aspects of system 100 can support mitigation of the effects of signal drift by delivering treatment based on variance 170-b. For instance, variance 170-b remains below a threshold variance value 171-b (and state 175-b remains in a low state '0'), even if the signal background noise increases above a threshold 166-b due to signal drift.

[0109] refer to Figure 4For example, graph 160-c illustrates an example of signal drift associated with the power 165-c of the bioelectrical signal 126. Aspects of system 100 can support mitigating the effects of signal drift by delivering treatment based on variance 170-c. For instance, referring to graph 161-c, the power 165-c only increases above the threshold 166-c after approximately 700 seconds, while the variance 170-c begins to exceed the threshold variance value 171-c at approximately 500 seconds. State 175-c transitions to a high state '1' at approximately 500 seconds, and computing device 102 can provide a control signal 155 for delivering treatment. Therefore, for example, unlike aDBS stimulation techniques based on power 165-c, computing device 102 (using vaADBS stimulation techniques) can deliver treatment even when the signal representing power 165-c is below the threshold 166-c.

[0110] refer to Figure 5 The variance 170-d did not exceed the threshold variance 171-d until about 515 seconds later, at which point state 175-d changed to the low state '0'.

[0111] Therefore, for example, aspects of system 100 can support treatment delivery with increased robustness to signal drift and measurement errors. For example, by mitigating the effects of signal drift, system 100 can support treatment delivery that more accurately reflects the biological condition of subject 148. Figure 5 The example case demonstrates how a variance of 170-d can be robust to noise / outliers.

[0112] Figure 6 Examples of system 100 are shown, illustrating various aspects of this disclosure.

[0113] System 100 includes a stimulation device 154 (e.g., an implantable medical device, an external stimulation device, etc.), a lead extension 156, one or more leads 157 (e.g., lead 157-a, lead 157-b) having a corresponding set of electrodes 150 (e.g., electrode 150-a, electrode 150-b), and a computing device 102. The computing device 102 can support programming functions associated with the stimulation device 154. The stimulation device 154 may include monitoring circuitry electrically connected to the electrodes 150 of the leads 157.

[0114] System 100 may support monitoring one or more bioelectrical signals 126 of subject 148. For example, stimulation device 154 may include sensing circuitry that senses bioelectrical signals 126 in one or more regions of anatomical element 149 (e.g., brain, neural tissue, etc.). In some aspects, the signals may be sensed by electrodes 150 and conducted to the sensing circuitry within stimulation device 154 via conductors within corresponding leads 157. In some aspects, control circuitry of stimulation device 154 or another device (e.g., computing device 102) monitors the bioelectrical signals 126 within anatomical element 149 of subject 148 to assess neural activation and / or perform other functions mentioned herein based on metrics (e.g., power 165, variance 170, etc.) described herein for the bioelectrical signals 126. Control circuitry of stimulation device 154 or another device (e.g., computing device 102) may control the delivery of electrical stimulation or other treatments to anatomical element 149 in a manner that treats a medical condition (e.g., encephalopathy) of subject 148 based on the variance 170 of the bioelectrical signals 126.

[0115] In some examples, the sensing circuitry of the stimulation device 154 may receive the bioelectrical signal 126 from the electrode 150 or other electrodes positioned to monitor the bioelectrical signal 126 of the subject 148 (e.g., if the housing of the stimulation device 154 is implanted or proximate to the anatomical element 149, electrodes of the housing may be used to sense the bioelectrical signal 126 and / or deliver stimulation to the anatomical element 149). The electrode 150 may also be used to deliver electrical stimulation from the stimulation generation circuitry of the stimulation device 154 to a target site within the anatomical element 149 and to sense the bioelectrical signal 126 within the anatomical element 149. In some aspects, the sensing circuitry of the stimulation device 154 may sense the bioelectrical signal 126 via one or more electrodes of the electrode 150, which may also be used to deliver electrical stimulation to the anatomical element 149. In some other aspects, one or more electrodes of the electrode 150 may be used to sense the bioelectrical signal 126, while one or more different electrodes 150 may be used to deliver electrical stimulation.

[0116] The bioelectrical signal 126 monitored by the stimulation device 154 (and whose variance 170 can be calculated as described herein) can reflect changes in the current generated by the sum of potential differences on the anatomical element 149 (e.g., on neural tissue). Examples of the monitored bioelectrical signal 126 include, but are not limited to, EEG signals, ECoG signals, MEG signals, and / or LFP signals sensed from or around one or more regions of the anatomical element 149.

[0117] According to exemplary aspects of this disclosure, stimulation device 154 can deliver treatment to any suitable portion of anatomical element 149. In some embodiments, system 100 can deliver treatment to subject 148 to manage subject 148's medical condition (e.g., neurological disorder). For example, system 100 can provide treatment to correct brain conditions and / or manage symptoms of neurodegenerative encephalopathy. Subject 148 can be a human patient. However, in some cases, aspects of this disclosure can support the application of the techniques described herein to other mammalian or non-mammal non-human patients.

[0118] The stimulation generating circuit of the stimulation device 154 can generate electrical stimulation therapy via electrodes 150 of leads 157 and deliver the electrical stimulation therapy to one or more areas of the anatomical elements 149 of the subject 148. Figure 6 In the example shown, system 100 may be referred to as a deep brain stimulation system, and stimulation device 154 may provide electrical stimulation therapy directly to tissue within anatomical element 149 (e.g., tissue site beneath the dura mater of anatomical element 149). In some other examples, lead 157 may be positioned to sense brain activity and / or deliver therapy to the surface of anatomical element 149 (e.g., the cortical surface of the brain) or located within subject 148 or along another location of the subject.

[0119] exist Figure 6 In the example shown, the stimulation device 154 may be implanted in a subcutaneous pouch below the collarbone of the subject 148. In other embodiments, the stimulation device 154 may be implanted in other areas of the subject 148, such as a subcutaneous pouch near the skull of the subject 148 in the abdomen or buttocks. The implanted lead extension 156 is coupled to the stimulation device 154 via a connector component (also referred to as a mandrel). The connector component may include, for example, electrical contacts that are electrically coupled to corresponding electrical contacts on the lead extension 156. The electrical contacts electrically couple electrodes 150 carried by the lead 157 to the stimulation device 154.

[0120] In an example where anatomical element 149 is the brain, lead extension 156 can pass along the neck of subject 148 and through the skull of subject 148 to the implantation site of stimulation device 154 within the chest cavity of subject 148 to reach anatomical element 149. Stimulation device 154 can be constructed of a biocompatible material resistant to corrosion and degradation by bodily fluids. Stimulation device 154 may include an hermetically sealed housing for substantially encapsulating control circuitry components such as processor 104, sensing circuitry, therapeutic programming circuitry, and memory 106. In some embodiments, stimulation device 154 and other components (e.g., lead 157) may be implanted in the patient's head (e.g., under the scalp) rather than in the chest and neck region.

[0121] System 100 can support configuring electrical stimulation to be delivered to one or more regions of anatomical element 149 based on one or more criteria, such as the type of patient symptom for which system 100 is implemented, the relative level of neural activation identified by variance 170, state 175 (vaDBS state), etc. In some cases, where anatomical element 149 is the brain, lead 157 can be implanted within both the right and left hemispheres of anatomical element 149. In other examples, one or both of leads 157 can be implanted within either the right or left hemisphere. Aspects of this disclosure support the implantation of one or more leads 157 at different sites (e.g., on or within the skull). Additionally, in some examples, lead 157 can be coupled to a single lead implanted within one hemisphere of anatomical element 149 or implanted through both the right and left hemispheres of anatomical element 149.

[0122] Lead 157 can be positioned to deliver electrical stimulation to one or more target tissue sites within anatomical element 149 to manage patient symptoms associated with the medical condition of subject 148. Lead 157 can be implanted to position electrode 150 at a desired location within anatomical element 149. Lead 157 can be placed within or along anatomical element 149 such that electrode 150 can provide electrical stimulation to target tissue sites within anatomical element 149 during treatment. In some embodiments, lead 157 can be positioned such that electrode 150 directly contacts or otherwise approaches target tissue in a region of anatomical element 149.

[0123] exist Figure 6 In the example shown, the electrode 150 of the lead 157 can be a ring electrode. A ring electrode can be capable of sensing an electric field and / or delivering an electric field to any tissue adjacent to the lead 157 (e.g., in all directions away from the outer periphery of the lead 157). In other examples, the electrode 150 of the lead 157 can have different configurations. For example, the electrode 150 of the lead 157 can have a complex electrode array geometry capable of generating an electric field of a certain shape. The complex electrode array geometry can include multiple electrodes (e.g., partially ring-shaped or segmented electrodes) around the periphery of each lead 157, rather than ring electrodes. In this way, EEG sensing and / or electrical stimulation can be associated with a specific direction originating from the lead 157 (e.g., less than the entire outer periphery of the lead 157) to enhance directional sensing and / or therapeutic efficacy and reduce potentially adverse side effects from stimulating large amounts of tissue under stimulation conditions. Thus, the electrodes can be positioned to sense from one side of the lead and stimulate target tissue while avoiding stimulation of non-target tissue.

[0124] In some embodiments, the housing of the stimulation device 154 may include one or more stimulation electrodes and / or sensing electrodes. For example, the housing may include conductive material that is exposed to the tissues of the subject 148 when the stimulation device 154 is implanted in the subject 148, or electrodes that can be attached to the housing. In alternative examples, the lead 157 may have, in addition to... Figure 6 Shapes other than the elongated cylinder shown. For example, lead 157 can be a flat strip lead, a spherical lead, a flexible lead, or any other type of shape that is effective in treating subject 148.

[0125] Under the control of processor 104, the stimulation generation circuit can generate stimulation signals for delivery to subject 148 via selected combinations of electrodes 150. Processor 104 can control the stimulation generation circuit to apply stimulation parameter values ​​(e.g., amplitude, pulse width, timing, pulse rate, etc.) associated with one or more stimulation programs, according to stimulation programs stored in memory 106. In some aspects, the stimulation generation circuit generates stimulation signals and delivers them to one or more target portions of anatomical element 149 via selected combinations of electrodes 150.

[0126] Lead 157 can be implanted at or within the target location of anatomical element 149 using any suitable technique. For example, in the case where anatomical element 149 is the brain, lead 157 can be implanted through a corresponding drill hole in the skull of subject 148 or through a common drill hole in the skull. Lead 157 can be placed anywhere within anatomical element 149 such that electrodes 150 of lead 157 can sense electrical activity in the region of anatomical element 149 and / or deliver electrical stimulation to target tissue for treatment. As used herein, lead 157 can be in the form of a probe having one or more electrodes and can be used in devices other than implantable devices.

[0127] In some aspects, the processing circuitry of system 100 (e.g., processor 104 of computing device 102 or processor of stimulation device 154) can control the delivery of electrical stimulation by activating, deactivating, increasing, or decreasing the intensity of electrical stimulation delivered to anatomical element 149 in association with titration electrical stimulation therapy. The processing circuitry (and / or the control circuitry of computing device 102 or stimulation device 154) can support the initiation, cessation, and / or modification of treatment delivery in any manner and based on any parameters or findings discussed herein.

[0128] System 100 may support the storage of multiple stimulation programs (e.g., a set of electrical stimulation parameter values) at a data repository (e.g., database 130). One or more of these stimulation programs may be associated with increasing and decreasing stimulation of neural tissue (e.g., neural activation). Stimulation device 154 or computing device 102 may select the stored stimulation programs, which define electrical stimulation parameter values ​​for delivering electrical stimulation to anatomical element 149.

[0129] According to an example aspect of this disclosure, the computing device 102, as a medical device, may be a separate application within a larger workstation or another multi-functional device, rather than a dedicated computing device. For example, the multi-functional device may be a laptop computer, tablet computer, workstation, cellular phone, personal digital assistant, or another computing device. The circuitry of the computing device 102 and other devices described herein may be control circuitry for performing functions as described herein, such as receiving signals from the stimulation device 154 via telemetry, measuring the amplitude or power 165 of the signals, calculating variance 170, and assessing the level of neural tissue activation.

[0130] Any of the functions described herein can be performed by the control circuitry of stimulation device 154, the control circuitry of computing device 102, or control circuitry distributed between stimulation device 154 and computing device 102. For example, the control circuitry of stimulation device 154 can perform sensing and amplitude (or power 165) measurement functions and transmit data 125 including amplitude (or power) information to the control circuitry of computing device 102, and computing device 102 can perform variance calculation and other functions described herein. The control circuitry of computing device 102 can determine treatment settings based on any of the information (e.g., power 165, variance 170, etc.) and transmit the treatment settings to the control circuitry of stimulation device 154, and the control circuitry of stimulation device 154 can deliver treatment based on these settings.

[0131] In this example, computing device 102 can be configured for use by a clinician, and can be used to transmit initial programming information to stimulation device 154. This initial information may include hardware information such as the type of lead 157, the arrangement of electrodes 150 on the lead 157, the location of the lead 157 within anatomical element 149, an initial procedure defining treatment parameter values, ranges, and / or thresholds for closed-loop treatment adjustments, and any other information that may be used to program into stimulation device 154. Computing device 102 may also be able to control the circuitry of stimulation device 154 to perform the functions described herein (e.g., functions related to sensing signals, calculating variance 170, assessing neural tissue activation, and / or delivering treatment).

[0132] Clinicians can also store treatment procedures within stimulation device 154 using computing device 102. During a programming session, clinicians can identify one or more stimulation procedures (e.g., neural activation procedures) that can effectively elicit therapeutic outcomes for a medical condition (e.g., encephalopathy). For example, a clinician can select one or more electrode combinations to deliver stimulation to anatomical element 149 to increase or decrease neural activation. During a programming session, clinicians can assess the efficacy of the one or more electrode combinations based on one or more findings from functional magnetic resonance imaging (MRI), patient self-reports, LEP, EEG, or other signals. In some examples, the processor of computing device 102 can calculate and display one or more therapeutic metrics for evaluating and comparing treatment procedures that can be used to deliver treatment from stimulation device 154 to subject 148.

[0133] The computing device 102 can also provide instructions to the subject 148 during treatment delivery, which can aid in assessing treatment efficacy. For example, after delivering electrical stimulation or sensing one or more metrics (e.g., power 165, variance 170, etc.) exceeding the target range, the computing device 102 can deliver one or more prompts to the subject 148 to assess whether the subject 148 is experiencing symptoms. In some examples, the prompts may include instructions to answer questions presented on the computing device 102. This information can be used to assess whether the delivered treatment is manifested in a form observable by the subject 148.

[0134] Figure 7 Examples of process flow 700 according to various aspects of this disclosure are shown. In some examples, process flow 700 may implement various aspects of the computing device 102 described herein.

[0135] In the following description of process flow 700, operations may be performed in a different order than those shown, or in a different order or at different times. Some operations may also be omitted from process flow 700, or one or more operations may be repeated, or other operations may be added to process flow 700.

[0136] It should be understood that although a is described as performing multiple operations of process flow 700, any device (e.g., another computing device 102 communicating with computing device 102) can perform the operations shown.

[0137] Process flow 700 can be implemented by a system including: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to perform the operations of process flow 700.

[0138] At 705, process flow 700 may include sensing bioelectrical signals from one or more electrodes that are in contact with or near the anatomical element.

[0139] In some examples, anatomical elements include neural tissue.

[0140] In some aspects, process flow 700 may include setting one or more sensing parameters associated with sensing a bioelectrical signal based on at least one of the following: the type associated with the bioelectrical signal; a variance value; a range associated with the variance value; and one or more of a plurality of measures.

[0141] At 710, process flow 700 may include determining variance values ​​associated with multiple measures of the bioelectrical signal.

[0142] In some examples, multiple metrics include the power value of the bioelectric signal.

[0143] At 715, process flow 700 may include generating control signals based on a comparison of variance values ​​with threshold variance values.

[0144] In some aspects, the generation of control signals may be based on a state associated with the delivery of treatment for an anatomical element. For example, at 720, process flow 700 may include setting a state associated with the delivery of treatment for an anatomical element based on at least one of the following: a comparison of a variance value with a threshold variance value; and a comparison of a variance value with a threshold range, wherein the generation of control signals is based on this state. In another example, at 720, process flow 700 may include setting a state associated with the delivery of treatment for an anatomical element based on a comparison of one or more of a plurality of measures with a threshold measure, wherein the generation of control signals is based on this state.

[0145] At 725, process flow 700 may include delivering treatment to an anatomical element in response to a control signal (e.g., via a treatment delivery device).

[0146] In some respects, delivery of treatment may include: providing electrical stimulation to anatomical elements; delivering to a subject a drug associated with the treatment of the anatomical element; or both.

[0147] In some aspects, at 730, process flow 700 may include setting one or more parameters associated with the delivery of treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of a plurality of measures; and a third treatment profile associated with the range corresponding to the plurality of measures.

[0148] Process flow 700 (and / or one or more of its operations) may be implemented, for example, by at least one processor or otherwise executed. The at least one processor may be the same as or similar to the processor(s) 104(s) of the computing device 102 described herein. Processors other than any processors described herein may also be used to execute process flow 700. At least one processor may execute operations of process flow 700 by executing elements stored in memory (e.g., memory 106). Elements stored in memory and executed by the processor may enable the processor to perform one or more operations of the functions shown in process flow 700. One or more portions of process flow 700 may be executed by the processor executing any contents of memory (e.g., image processing 120, segmentation 122, transformation 124, and / or registration 128).

[0149] As stated above, this disclosure covers companies with fewer than Figure 7 The methods for all steps identified in the diagram (and the corresponding descriptions in process flow 700), as well as those exceeding... Figure 7 The method includes additional steps of those steps identified in (and the corresponding description of process flow 700). This disclosure also covers methods that include one or more steps from one method described herein and one or more steps from another method described herein. Any relevance described herein may be or includes registration or any other relevance.

[0150] The foregoing is not intended to limit this disclosure to the one or more forms disclosed herein. For example, in the foregoing specific embodiments, various features of this disclosure are grouped together in one or more aspects, implementations, and / or configurations for the purpose of fluent expression. Features of aspects, implementations, and / or configurations of this disclosure may be combined in alternative aspects, implementations, and / or configurations other than those discussed above. The method of this disclosure is not to be construed as reflecting an intention that the claims would require more features than expressly stated in each claim. Rather, as reflected in the claims, the inventive aspect possesses fewer features than all the features possessed by a single foregoing disclosure aspect, implementation, and / or configuration. Therefore, the claims are thus incorporated into the specific embodiments, wherein each claim itself can be considered a separate preferred embodiment of this disclosure.

[0151] Furthermore, while the foregoing has included descriptions of one or more aspects, implementations, and / or configurations, as well as certain variations and modifications, other variations, combinations, and modifications are also within the scope of this disclosure, for example, those that, upon understanding this disclosure, would be within the skill and knowledge of someone skilled in the art. The foregoing is intended to obtain rights to alternative aspects, implementations, and / or configurations, including those within the permitted scope (including alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps of those claimed) (whether such alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps are disclosed herein), and is not intended to publicly offer any patentable subject matter.

[0152] Examples of aspects of this disclosure include: A system includes: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: sense a bioelectrical signal from one or more electrodes in contact with or near an anatomical element; determine a variance value associated with a plurality of measures of the bioelectrical signal; generate a control signal based on a comparison of the variance value with a threshold variance value; and deliver treatment to the anatomical element in response to the control signal.

[0153] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value; and a comparison of the variance value with a threshold range, wherein the generation of control signals is based on the state.

[0154] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold measure, wherein the generation of the control signal is based on the state.

[0155] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0156] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more sensing parameters associated with sensing the bioelectric signal based on at least one of the following: the type associated with the bioelectric signal; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0157] In any aspect of this article, the instructions which can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: provide electrical signal stimulation to the anatomical element; and deliver a drug associated with the treatment of the anatomical element to the subject.

[0158] Any aspect of this article, wherein the plurality of measures includes the power value of the bioelectric signal.

[0159] Any aspect of this article, in which the anatomical element includes neural tissue.

[0160] A system includes: a treatment delivery device; a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: sense a bioelectrical signal from one or more electrodes in contact with or near an anatomical element; determine a variance value associated with a plurality of measures of the bioelectrical signal; generate a control signal based on a comparison of the variance value with a threshold variance value; and deliver treatment to the anatomical element via the treatment delivery device in response to the control signal.

[0161] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value; and a comparison of the variance value with a threshold range, wherein the generation of control signals is based on the state.

[0162] In any aspect of this document, wherein these instructions may be further executed by the processor to perform the following operations: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold, wherein the generation of the control signal is based on the state.

[0163] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0164] In any aspect of this document, wherein these instructions may be further executed by the processor to set one or more sensing parameters associated with sensing the bioelectric signal based on at least one of the following: the type associated with the bioelectric signal; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0165] In any aspect of this article, the instructions which can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: provide electrical signal stimulation to the anatomical element; deliver a drug associated with the treatment of the anatomical element to the subject; or both.

[0166] A method includes: sensing a bioelectrical signal from one or more electrodes in contact with or near an anatomical element via one or more sensors; calculating a variance value associated with a plurality of measures of the bioelectrical signal; generating a control signal based on a comparison of the variance value with a threshold variance value; transmitting the control signal electronically; and causing treatment to be delivered to the anatomical element in response to the control signal.

[0167] Any aspect of this document further includes: setting a state associated with the delivery of treatment for the anatomical element based on at least one of the following: a comparison of the variance value with a threshold variance value; and a comparison of the variance value with a threshold range, wherein the generation of the control signal is based on the state.

[0168] Any aspect of this document further includes: setting a state associated with the delivery of treatment for the anatomical element based on a comparison of one or more of the plurality of measures with a threshold, wherein the generation of the control signal is based on the state.

[0169] Any aspect of this document further includes setting one or more parameters associated with the delivery of the treatment based on at least one of the following: a treatment profile associated with the variance value; a second treatment profile associated with the range associated with the variance value; and a third treatment profile associated with one or more of the plurality of measures.

[0170] Any aspect of this document further includes setting one or more sensing parameters associated with sensing the bioelectric signal based on at least one of the following: the type associated with the bioelectric signal; the variance value; the range associated with the variance value; and one or more of the plurality of measures.

[0171] Any aspect of this article further includes: providing electrical signal stimulation to the anatomical element; and delivering a drug associated with the treatment of the anatomical element to the subject.

[0172] Any one aspect in combination with any one or more other aspects.

[0173] Any one or more of the features disclosed in this article.

[0174] As is the case with any one or more of the features substantially disclosed herein.

[0175] Combination of any one or more features as substantially disclosed herein with any one or more other features as substantially disclosed herein.

[0176] Any of these aspects / features / implementations in combination with any one or more other aspects / features / implementations.

[0177] Use of any one or more aspects or features as disclosed herein.

[0178] It should be understood that any feature described herein may be claimed in combination with any other feature(s) as described herein, regardless of whether such features are derived from the same implementation described.

[0179] The phrases “at least one,” “one or more,” “or,” and “and / or” are open-ended expressions that are both connective and discrete in their operation. For example, each of the expressions “at least one of A, B, and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” “A, B, and / or C,” and “A, B, or C” refers to A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together.

[0180] The term "a / an" refers to one or more of the entities in question. Thus, the terms "a" (or "an"), "one or more," and "at least one" are used interchangeably herein. It should also be noted that the terms "comprising," "including," and "having" are used interchangeably.

[0181] As used herein, the term "automatic" and its variations refer to any process or operation that is typically continuous or semi-continuous, performed without substantial human input. However, a process or operation can be automatic even if its execution uses substantial or non-substantial human input, provided that input is received prior to its execution. Human input is considered substantial if it influences how a process or operation is performed. Human input that consents to the execution of a process or operation is not considered "substantial."

[0182] Various aspects of this disclosure may take the form of a wholly hardware implementation, a wholly software (including firmware, resident software, microcode, etc.) implementation, or a combination of software and hardware implementations, all of which may generally be referred to herein as “circuit,” “module,” or “system.” Any combination of one or more computer-readable media may be used. Computer-readable media may be computer-readable signal media or computer-readable storage media.

[0183] Computer-readable storage media can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable media (a non-exhaustive list) will include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the context of this document, computer-readable storage media can be any tangible medium that can contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0184] Computer-readable signal media may include propagated data signals in which (e.g., in baseband or as part of a carrier wave) computer-readable program code is embodied. Such propagated signals may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. Computer-readable signal media may be any computer-readable medium that is not a computer-readable storage medium and may transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic, cable, RF, etc., or any suitable combination thereof.

[0185] As used herein, the terms “determine,” “estimate,” “calculate,” and their variations are used interchangeably and include any type of method, process, mathematical operation, or technique.

Claims

1. A system comprising: processor; as well as A memory storing instructions that, when executed by the processor, cause the processor to perform the following operations: Sensing one or more electrical signals from one or more electrodes that are in contact with or near the anatomical element; Determine the variance values ​​associated with multiple measures of the one or more electrical signals; A control signal is generated based on the comparison between the variance value and the threshold variance value. as well as Treatment is delivered targeting the anatomical elements in response to the control signal.

2. The system according to claim 1, wherein, The instructions can be further executed by the processor to perform the following operations: Set the state associated with the delivery of treatment targeting this anatomical element based on at least one of the following: The comparison result between the variance value and one or more threshold variance values; and The variance value is compared with one or more threshold ranges. The generation of the control signal is based on the state.

3. The system according to any of the preceding claims, wherein, The instructions can be further executed by the processor to perform the following operations: A state associated with the delivery of treatment for the anatomical element is set based on a comparison of one or more of the plurality of metrics with a threshold metric, wherein the generation of the control signal is performed based on the state.

4. The system according to any of the preceding claims, wherein, The instructions can be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: Treatment profiles associated with the variance values; A second treatment profile associated with the range associated with the variance value; as well as A third treatment profile associated with one or more of the plurality of metrics.

5. The system according to any of the preceding claims, wherein, The instructions can be further executed by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of the following: The type associated with the one or more electrical signals; The variance value; The range associated with the variance value; and One or more of the plurality of metrics.

6. The system according to any of the preceding claims, wherein, The instructions that can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: Provide electrical stimulation to the anatomical elements; and Administering to the subject a drug associated with the treatment of the anatomical element.

7. The system according to any of the preceding claims, wherein, The plurality of metrics include the power values ​​of the one or more electrical signals.

8. The system according to any of the preceding claims, wherein, The anatomical elements include neural tissue.

9. A system comprising: Treatment delivery devices; processor; as well as A memory storing instructions that, when executed by the processor, cause the processor to perform the following operations: Sensing one or more electrical signals from one or more electrodes that are in contact with or near the anatomical element; Determine the variance values ​​associated with multiple measures of the one or more electrical signals; A control signal is generated based on the comparison result between the variance value and the threshold variance value. as well as Treatment targeting the anatomical element is delivered via the treatment delivery device in response to the control signal.

10. The system according to claim 9, wherein, The instructions can be further executed by the processor to perform the following operations: Set the state associated with the delivery of treatment targeting this anatomical element based on at least one of the following: The comparison result between the variance value and one or more threshold variance values; and The variance value is compared with one or more threshold ranges. The generation of the control signal is based on the state.

11. The system according to claim 9 or 10, wherein, The instructions can be further executed by the processor to perform the following operations: A state associated with the delivery of treatment for the anatomical element is set based on a comparison of one or more of the plurality of metrics with a threshold, wherein the generation of the control signal is performed based on the state.

12. The system according to any one of claims 9 to 11, wherein, The instructions can be further executed by the processor to set one or more parameters associated with the delivery of the treatment based on at least one of the following: Treatment profiles associated with the variance values; A second treatment profile associated with the range associated with the variance value; as well as A third treatment profile associated with one or more of the plurality of metrics.

13. The system according to any one of claims 9 to 12, wherein, The instructions can be further executed by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of the following: The type associated with the one or more electrical signals; The variance value; The range associated with the variance value; and One or more of the plurality of metrics.

14. The system according to any one of claims 9 to 13, wherein, The instructions that can be executed by the processor to deliver the treatment can be further executed by the processor to perform the following operations: Provide electrical stimulation to the anatomical elements; Administering to a subject a drug associated with the treatment of the anatomical element; or both.

15. A method comprising: One or more electrical signals are sensed from one or more electrodes that are in contact with or near the anatomical element via one or more sensors; Calculate the variance associated with multiple measures of the one or more electrical signals; A control signal is generated based on the comparison result between the variance value and the threshold variance value. The control signal is transmitted electronically; as well as This enables the delivery of treatment to the anatomical element in response to the control signal.

16. The method of claim 15, further comprising: The state associated with the delivery of treatment targeting the anatomical element is set based on at least one of the following: The comparison result between the variance value and one or more threshold variance values; and The variance value is compared with one or more threshold ranges. The generation of the control signal is based on the state.

17. The method according to claim 15 or 16, further comprising: The state associated with the delivery of treatment targeting the anatomical element is set based on a comparison of one or more of the plurality of metrics with a threshold. The generation of the control signal is based on the state.

18. The method according to any one of claims 15 to 17, further comprising: Set one or more parameters associated with the delivery of the treatment based on at least one of the following: Treatment profiles associated with the variance values; A second treatment profile associated with the range associated with the variance value; as well as A third treatment profile associated with one or more of the plurality of metrics.

19. The method according to any one of claims 15 to 18, further comprising: One or more sensing parameters associated with sensing the one or more electrical signals are set based on at least one of the following: The type associated with the one or more electrical signals; The variance value; The range associated with the variance value; and One or more of the plurality of metrics.

20. The method according to any one of claims 15 to 19, further comprising: Provide electrical stimulation to the anatomical elements; as well as Administering to the subject a drug associated with the treatment of the anatomical element.

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